Abstract
From the standpoint of fundamental consideration of experimental data on the loading kinetics of multilayer continuums based on mechanical tests and acoustic emission measurements, large amounts of digital information on the parameters of AE signals during four-point bending of metal-based epoxy coatings have been processed and systematized. Features of the structure of the AE spectrum under load are reflected in the large size of the input data associated with the material’s response to external force action and the generation of different AE signals from media. The principal components method was used to analyze the acoustic emission spectra under loading of multilayer structures. The relationship between the kinetics of structural changes at various stages of deformation of materials and the components of the acoustic spectrum is revealed and quantitatively described. Visualization of acoustic emission signals in the time domain reveals a tendency to increase their impulsiveness. The processing of the experimental data using the principal component method made it possible to cluster the AE signals. The correspondence of clusters with the stages of strain hardening is established. The results obtained can be used in the AE study of the stages of loading processes and diagnostics of multilayer structures. The main characteristics of the impulsivity of AE signals under loading of continuous conjugated media are calculated. Recommendations are given on the use of specific components of the principal component method as indicators of the state of strain hardening of materials of multilayer structures.
Keywords
- Multilayer media
- Mechanical testing
- Acoustic emission
- Loading
- Principal component method
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References
ASTM E2734-04. Standard guide for acoustic emission system performance verification
ISO 13380-2002. Diagnostic of machine of using performance parameter. General guidelines
Abdulaziz, A., McCrory, J., Holford, K., Elsabbagh, A., Hedaya, M.: Experimental three-point bending test of glass fibre aluminium honeycomb sandwich panel with acoustic emission damage assessment. Insight Nondestr. Test. Condition Monit. 63(12), 727–733 (2021). https://doi.org/10.1784/insi.2021.63.12.727
Al-Jumaili, S., Eaton, M., Holford, K., McCrory, J., Pullin, R.: Damage characterisation in composite materials under buckling test using acoustic emission waveform clustering technique. In: 53rd Annual Conference of The British Institute of Non-destructive Testing (2014)
Babichev, S., Škvor, J.: Technique of gene expression profiles extraction based on the complex use of clustering and classification methods. Diagnostics 10(8) (2020). https://doi.org/10.3390/diagnostics10080584
Babichev, S., Lytvynenko, V., Skvor, J., Korobchynskyi, M., Voronenko, M.: Information technology of gene expression profiles processing for purpose of gene regulatory networks reconstruction. In: Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018, pp. 336–341 (2018). https://doi.org/10.1109/DSMP.2018.8478452
Babichev, S.A., Kornelyuk, A.I., Lytvynenko, V.I., Osypenko, V.V.: Computational analysis of microarray gene expression profiles of lung cancer. Biopolymers Cell 32(1), 70–79 (2016). https://doi.org/10.7124/bc.00090F
Behnia, A., Chai, H., GhasemiGol, M., Sepehrinezhad, A., Mousa, A.: Advanced damage detection technique by integration of unsupervised clustering into acoustic emission. Eng. Fract. Mech. 210, 212–227 (2019). https://doi.org/10.1016/j.engfracmech.2018.07.005
Buketov, A., Brailo, M., Yakushchenko, S., Sapronov, O., Smetankin, S.: The formulation of epoxy-polyester matrix with improved physical and mechanical properties for restoration of means of sea and river transport. J. Mar. Eng. Technol. 19(3), 109–114 (2020). https://doi.org/10.1080/20464177.2018.1530171
Cho, H., Shoji, N., Ito, H.: Acoustic emission generation behavior in A7075-T651 and A6061-T6 aluminum alloys with and without cathodic hydrogen charging under cyclic loading. J. Nondestr. Eval. 37(4), 1–7 (2018). https://doi.org/10.1007/s10921-018-0536-7
Dmitriev, A., Polyakov, V., Kolubaev, E.: Digital processing of acoustic emission signals in the study of welded compounds in metal alloys. High-Perform. Comput. Syst. Technol. 4(1), 32–40 (2020)
Dmitriev, A., Polyakov, V., Lependin, A.: Investigation of plastic deformation of aluminum alloys using wavelet transforms of acoustic emission signals. Russ. J. Nondestr. Test. 8(1), 33–36 (2018). https://doi.org/10.22226/2410-3535-2018-1-33-36
Dmitriev, A., Polyakov, V., Ruder, D.: Application of the principal component method to the study of acoustic emission signals in aluminum alloys. News Altai State Univ. Phys. 1(99), 19–23 (2018). https://doi.org/10.14258/izvasu(2018)1-02
Fotouhi, M., Sadeghi, S., Jalalvand, M., Ahmadi, M.: Analysis of the damage mechanisms in mixed-mode delamination of laminated composites using acoustic emission data clustering. J. Thermoplast. Compos. Mater. 30(3), 318–340 (2017). https://doi.org/10.1177/0892705715598362
Gagar, D., Foote, P., Irving, P.: Effects of loading and sample geometry on acoustic emission generation during fatigue crack growth: implications for structural health monitoring. Int. J. Fatigue 81, 117–127 (2015). https://doi.org/10.1016/j.ijfatigue.2015.07.024
Hao, W., Huang, Z., Xu, Y., Zhao, G., Chen, H., Fang, D.: Acoustic emission characterization of tensile damage in 3D braiding composite shafts. Polym. Test. 81, 106176 (2020). https://doi.org/10.1016/j.polymertesting.2019.106176
Harizi, W., Chaki, S., Bourse, G., Ourak, M.: Damage mechanisms assessment of glass fiber-reinforced polymer (GFRP) composites using multivariable analysis methods applied to acoustic emission data. Compos. Struct. 289(1), 115470 (2022). https://doi.org/10.1016/j.compstruct.2022.115470
Hase, A.: Early detection and identification of fatigue damage in thrust ball bearings by an acoustic emission technique. Lubricants 8(3), 37 (2020). https://doi.org/10.3390/lubricants8030037
He, M., He, D., Qu, Y.: A new signal processing and feature extraction approach for bearing fault diagnosis using ae sensors. J. Fail. Anal. Prev. 16(5), 821–827 (2016). https://doi.org/10.1007/s11668-016-0155-5
Hongwu, Q., Chao, Z., Xian, Z., Qinyin, F.: Research of acoustic emission testing method with application to monitored for wind turbines. Int. J. Multimedia Ubiquit. Eng. 10(1), 109–118 (2015)
Huang, J., Zhang, Z., Han, C., Yang, G.: Identification of deformation stage and crack initiation in tc11 alloys using acoustic emission. Appl. Sci. 10(11), 3674 (2020). https://doi.org/10.3390/app10113674
Lavanya, S., Mahadevan, S., Mukhopadhyay, C.K., Kumar, S.A.: Acoustic emission during press-brake bending of ss 304L sheets and its correlation with residual stress distribution after bending. J. Mater. Eng. Perform. (5), 1–12 (2021). https://doi.org/10.1007/s11665-021-06250-w
Lependin, A.A., Polyakov, V.V.: Scaling of the acoustic emission characteristics during plastic deformation and fracture. Tech. Phys. 59(7), 1041–1045 (2014). https://doi.org/10.1134/S1063784214070184
Li, D., Tan, M., Zhang, S., Ou, J.: Stress corrosion damage evolution analysis and mechanism identification for prestressed steel strands using acoustic emission technique. Struct. Control Health Monit. 25(8), e2189 (2018). https://doi.org/10.1002/stc.2189
Louda, P., Marasanov, V., Sharko, A., Stepanchikov, D., Sharko, A.: The theory of similarity and analysis of dimensions for determining the state of operation of structures under difficult loading conditions. materials. Materials 15(3), 1191 (2022). https://doi.org/10.3390/ma15031191
Louda, P., Sharko, A., Stepanchikov, D.: An acoustic emission method for assessing the degree of degradation of mechanical properties and residual life of metal structures under complex dynamic deformation stresses. Materials 14(9), 2090 (2021). https://doi.org/10.3390/ma14092090
Lu, D., Yu, W.: Correlation analysis between acoustic emission signal parameters and fracture stress of wool fiber. Text. Res. J. 89(21–22), 4568–4580 (2019). https://doi.org/10.1177/0040517519838057
Lytvynenko, V., Lurie, I., Krejci, J., Voronenko, M., Savina, N., Taif, M.A.: Two step density-based object-inductive clustering algorithm. In: CEUR Workshop Proceedings, vol. 2386, pp. 117–135 (2019)
Babichev, S., Lytvynenko, V. (eds.): ISDMCI 2021. LNDECT, vol. 77. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-82014-5
Marasanov, V., Sharko, A.: Energy spectrum of acoustic emission signals in complex media. J. Nano- Electro. Phys. 9(4), 04024-1–04024-5 (2017). https://doi.org/10.21272/jnep.9(4).04024
Marasanov, V., Sharko, A.: The energy spectrum of the acoustic emission signals of nanoscale objects. J. Nano-electron. Phys. 9(2), 02012-1–02012-4 (2017). https://doi.org/10.21272/jnep.9(2).02012
Marasanov, V.V., Sharko, A.V., Sharko, A.A.: Boundary-value problems of determining the energy spectrum of acoustic emission signals in conjugate continuous media. Cybern. Syst. Anal. 55(5), 851–859 (2019). https://doi.org/10.1007/s10559-019-00195-8
Babichev, S., Peleshko, D., Vynokurova, O. (eds.): DSMP 2020. CCIS, vol. 1158. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61656-4
Marasanov, V., Stepanchikov, D., Sharko, O., Sharko, A.: Operator of the dynamic process of the appearance of acoustic emission signals during deforming the structure of materials. In: IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO), pp. 646–650 (2020). https://doi.org/10.1109/ELNANO50318.2020.9088893
Babichev, S., Lytvynenko, V., Wójcik, W., Vyshemyrskaya, S. (eds.): ISDMCI 2020. AISC, vol. 1246. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-54215-3
Mengyu, C., Xinglong, H., Zaoxiao, Z., Quan, D.: Identification and prediction of fatigue crack growth under different stress ratios using acoustic emission data. Int. J. Fatigue 160(106860) (2022). https://doi.org/10.1016/j.ijfatigue.2022.106860
Mengyu, C., Zaoxiao, Z., Quan, D.: A new qualitative acoustic emission parameter based on Shannon’s entropy for damage monitoring. Mech. Syst. Sig. Process. 100(1), 617–629 (2018). https://doi.org/10.1016/j.ymssp.2017.08.007
Mieza, J., Oliveto, M., López Pumarega, M., Armeite, M., Ruzzante, J., Piotrkowski, R.: Identification of ae bursts by classification of physical and statistical parameters. In: AIP Conference Proceedings, vol. 760, p. 1174 (2005). https://doi.org/10.1063/1.1916805
Muir, C., et al.: Damage mechanism identification in composites via machine learning and acoustic emission. NPJ Comput. Mater. 7(95) (2021). https://doi.org/10.1038/s41524-021-00565-x
Nedoseka, A., Nedoseka, S., Markashova, L., Kushnareva, O.: On identification of structural changes in materials at fracture by acoustic emission data. Tech. Diagn. Nondestr. Test. (4), 9–13 (2016). https://doi.org/10.15407/tdnk2016.04.02
Roundi, W., El Mahi, A., El Gharad, A., Rebiere, J.L.: Acoustic emission monitoring of damage progression in glass/epoxy composites during static and fatigue tensile tests. Appl. Acoust. 132, 124–134 (2018). https://doi.org/10.1016/j.apacoust.2017.11.017
Saidi, L., Ali, J., Bechhoefer, E., Benbouzid, M.: Wind turbine high-speed shaft bearings health prognosis through a spectral kurtosis-derived indices and SVR. Appl. Acoust. 120, 1–8 (2017). https://doi.org/10.1016/j.apacoust.2017.01.005
Sapronov, O., Buketov, A., Maruschak, P., et al.: Research of crack initiation and propagation under loading for providing impact resilience of protective coating. Funct. Mater. 26(1), 114–120 (2019). https://doi.org/10.15407/fm26.01.114
Sharko, M., Gonchar, O., Tkach, M., et al.: Intellectual information technologies of the resources management in conditions of unstable external environment. In: Babichev, S., Lytvynenko, V. (eds.) ISDMCI 2021. LNDECT, vol. 1158, pp. 519–533. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-82014-5_35
Sharko, M., Gusarina, N., Petrushenko, N.: Information-entropy model of making management decisions in the economic development of the enterprises. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds.) ISDMCI 2019. AISC, vol. 1020, pp. 304–314. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-26474-1_22
Tian, Y., Yu, R., Zhang, Y., Zhao, X.: Application of acoustic emission characteristics in damage identification and quantitative evaluation of limestone. Adv. Eng. Sci. 52(3), 115–122 (2020). https://doi.org/10.15961/j.jsuese.201900315
Wuriti, G., Chattopadhyaya, S., Thomas, T.: Acoustic emission test method for investigation of m250 maraging steel pressure vessels for aerospace applications. In: Materials Today: Proceedings, vol. 49, pp. 2176–2182 (2022). https://doi.org/10.1016/j.matpr.2021.09.087
Zarif Karimi, N., Minak, G., Kianfar, P.: Analysis of damage mechanisms in drilling of composite materials by acoustic emission. Compos. Struct. 131, 107–114 (2015). https://doi.org/10.1016/j.compstruct.2015.04.025
Özaslan, E., Yetgin, A., Acar, B., Güler, M.: Damage mode identification of open hole composite laminates based on acoustic emission and digital image correlation methods. Compos. Struct. 274, 114299 (2021). https://doi.org/10.1016/j.compstruct.2021.114299
Zhang, Y., Luo, H., Li, J., Lv, J., Zhang, Z., Ma, Y.: An integrated processing method for fatigue damage identification in a steel structure based on acoustic emission signals. J. Mater. Eng. Perform. 26(4), 1784–1791 (2017). https://doi.org/10.1007/s11665-017-2616-8
Zhang, Y., Zhou, B., Yu, F., Chen, C.: Cluster analysis of acoustic emission signals and infrared thermography for defect evolution analysis of glass/epoxy composites. Infrared Phys. Technol. 112, 103581 (2021). https://doi.org/10.1016/j.infrared.2020.103581
Zhao, G., Zhang, L., Tang, C., Hao, W., Luo, Y.: Clustering of ae signals collected during torsional tests of 3D braiding composite shafts using PCA and FCM. Compos. B Eng. 161, 547–554 (2019). https://doi.org/10.1016/j.compositesb.2018.12.145
Shen, G., Zhang, J., Wu, Z. (eds.): WCAE 2017. SPP, vol. 218. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12111-2
Zhou, W., Zhao, W., Zhang, Y., Ding, Z.: Cluster analysis of acoustic emission signals and deformation measurement for delaminated glass fiber epoxy composites. Compos. Struct. 195, 349–358 (2018). https://doi.org/10.1016/j.compstruct.2018.04.081
Zou, S., Yan, F., Yang, G., Sun, W.: The identification of the deformation stage of a metal specimen based on acoustic emission data analysis. Sensors 17(4), 789 (2017). https://doi.org/10.3390/s17040789
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Louda, P., Sharko, O., Stepanchikov, D., Sharko, A. (2023). Features of the Application of the Principal Component Method to the Study of Acoustic Emission Signals Under Loading of Multilayer Structures. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_27
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